In large companies such as Siemens and Statoil monitoring tasks are of great importance, e.g., Siemens does monitoring of turbines and Statoil of oil behaviour in wells. This tasks bring up importance of both streaming and historical (temporal) data in the Big Data challenge for industries. We present the Optique project that addresses this problem by developing an Ontology Based Data Access (O...

Data mining has been widely used to identify potential customers for a new product or service. In this article is done a study of previous work relating to the application of data mining methodologies for software projects, specifically for direct marketing projects. Several data sets of demographic and historical customer purchases data available for evaluation of algorithms in this area, some...

False alarms triggered by security sensors incur high costs for all parties involved. According to police reports, a large majority of alarms are false. Recent advances in machine learning can enable automatically classifying alarms. However, building a scalable alarm verification system is a challenge, since the system needs to: (1) process thousands of alarms in real-time, (2) classify false ...

Since the population is growing, the need for high quality and efficient healthcare, both at home and in hospital, is becoming more important. This paper presents the innovative wireless sensor network based Mobile Real-time Health care Monitoring (WMRHM) framework which has the capacity of giving health predictions online based on continuously monitored real time vital body signals. Developmen...

This paper introduces Debellor (www.debellor.org) – an open source extensible data mining platform with stream-oriented architecture, where all data transfers between elementary algorithms take the form of a stream of samples. Data streaming enables implementation of scalable algorithms, which can efficiently process large volumes of data, exceeding available memory. This is very important for ...

Data mining is the process of finding patterns or correlations from a different data set and changing it into the useful information. Clustering is dividing the data into groups that are similar in behavior among the data sets in a group and distinct across the groups. Data Stream mining is very important and challenging problem, because in business transactions we need to make better manageria...

Micro-blogging service Twitter is a lucrative source for data mining applications on global sentiment. But due to the omnifariousness of the subjects mentioned in each data item; it is inefficient to run a data mining algorithm on the raw data. This paper discusses an algorithm to accurately classify the entire stream in to a given number of mutually exclusive collectively exhaustive streams up...

To achieve the concept of smart roads, intelligent sensors are being placed on the roadways to collect real-time traffic streams. Traditional method is not a real-time response, and incurs high communication and storage costs. Existing distributed stream mining algorithms do not consider the resource limitation on the lightweight devices such as sensors. In this paper, we propose a distributed ...

While several thousand square kilometers of land area have been subject to surface mining in the Central Appalachians, no reliable estimate exists for how much coal is produced per unit landscape disturbance. We provide this estimate using regional satellite-derived mine delineations and historical county-level coal production data for the period 1985-2005, and further relate the aerial extent ...

At present a growing number of applications that generate massive streams of data need intelligent data processing and online analysis. Real-time surveillance systems, telecommunication systems, sensor networks and other dynamic environments are such examples. The imminent need for turning such data into useful information and knowledge augments the development of systems, algorithms and framew...